{"id":"https://openalex.org/W4225989372","doi":"https://doi.org/10.21437/interspeech.2022-10060","title":"User-Level Differential Privacy against Attribute Inference Attack of Speech Emotion Recognition on Federated Learning","display_name":"User-Level Differential Privacy against Attribute Inference Attack of Speech Emotion Recognition on Federated Learning","publication_year":2022,"publication_date":"2022-09-16","ids":{"openalex":"https://openalex.org/W4225989372","doi":"https://doi.org/10.21437/interspeech.2022-10060"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2022-10060","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2022-10060","pdf_url":null,"source":{"id":"https://openalex.org/S4363604309","display_name":"Interspeech 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2022","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2204.02500","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100567944","display_name":"Tiantian Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tiantian Feng","raw_affiliation_strings":["Signal Analysis and Interpretation Lab (SAIL), University of Southern California"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Signal Analysis and Interpretation Lab (SAIL), University of Southern California","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086205666","display_name":"Raghuveer Peri","orcid":"https://orcid.org/0000-0002-1010-065X"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raghuveer Peri","raw_affiliation_strings":["Signal Analysis and Interpretation Lab (SAIL), University of Southern California"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Signal Analysis and Interpretation Lab (SAIL), University of Southern California","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010028928","display_name":"Shrikanth Narayanan","orcid":"https://orcid.org/0000-0002-1052-6204"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shrikanth Narayanan","raw_affiliation_strings":["Signal Analysis and Interpretation Lab (SAIL), University of Southern California"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Signal Analysis and Interpretation Lab (SAIL), University of Southern California","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":5.9045,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.97076893,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"5055","last_page":"5059"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9154999852180481,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9043999910354614,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8279494047164917},{"id":"https://openalex.org/keywords/adversary","display_name":"Adversary","score":0.773833155632019},{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.7609971761703491},{"id":"https://openalex.org/keywords/information-leakage","display_name":"Information leakage","score":0.5732398629188538},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5724560022354126},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.532398521900177},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.5045193433761597},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.49184584617614746},{"id":"https://openalex.org/keywords/intuition","display_name":"Intuition","score":0.47596853971481323},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4221554100513458},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3736698031425476},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.27860838174819946}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8279494047164917},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.773833155632019},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.7609971761703491},{"id":"https://openalex.org/C2779201187","wikidata":"https://www.wikidata.org/wiki/Q2775060","display_name":"Information leakage","level":2,"score":0.5732398629188538},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5724560022354126},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.532398521900177},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.5045193433761597},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.49184584617614746},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.47596853971481323},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4221554100513458},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3736698031425476},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27860838174819946},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/interspeech.2022-10060","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2022-10060","pdf_url":null,"source":{"id":"https://openalex.org/S4363604309","display_name":"Interspeech 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2022","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2204.02500","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.02500","pdf_url":"https://arxiv.org/pdf/2204.02500","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2204.02500","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.02500","pdf_url":"https://arxiv.org/pdf/2204.02500","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1873763122","https://openalex.org/W2030931454","https://openalex.org/W2134707158","https://openalex.org/W2146334809","https://openalex.org/W2161073241","https://openalex.org/W2167372639","https://openalex.org/W2342475039","https://openalex.org/W2473418344","https://openalex.org/W2535690855","https://openalex.org/W2963456518","https://openalex.org/W2964162474","https://openalex.org/W2970408908","https://openalex.org/W2972943112","https://openalex.org/W3013045184","https://openalex.org/W3016632787","https://openalex.org/W3104696513","https://openalex.org/W3128515475","https://openalex.org/W3197580070","https://openalex.org/W3204999899","https://openalex.org/W4226255887","https://openalex.org/W4318619660"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W4246396837","https://openalex.org/W2482350142","https://openalex.org/W3176240006","https://openalex.org/W3126451824","https://openalex.org/W3038283795","https://openalex.org/W4296973715","https://openalex.org/W4387193529","https://openalex.org/W3093310219","https://openalex.org/W3101646702"],"abstract_inverted_index":{"Many":[0],"existing":[1],"privacy-enhanced":[2],"speech":[3,13],"emotion":[4],"recognition":[5],"(SER)":[6],"frameworks":[7],"focus":[8],"on":[9,72],"perturbing":[10],"the":[11,32,37,70,102,109,113,133,143,146,150,157,160,164,172,176,182],"original":[12],"data":[14,71],"through":[15],"adversarial":[16],"training":[17],"within":[18],"a":[19],"centralized":[20],"machine":[21,57],"learning":[22,44,48,58],"setup.":[23],"However,":[24,156],"this":[25,96],"privacy":[26,55,67,80,105,110,121,124],"protection":[27],"scheme":[28],"can":[29,34,135],"fail":[30],"since":[31],"adversary":[33,151],"still":[35],"access":[36],"perturbed":[38],"data.":[39],"In":[40,95],"recent":[41],"years,":[42],"distributed":[43],"algorithms,":[45],"especially":[46],"federated":[47],"(FL),":[49],"have":[50],"gained":[51],"popularity":[52],"to":[53,65,100,171,180],"protect":[54],"in":[56,107,116,184],"applications.":[59],"While":[60],"FL":[61,165],"provides":[62,119],"good":[63],"intuition":[64],"safeguard":[66],"by":[68],"keeping":[69,142],"local":[73],"devices,":[74],"prior":[75],"work":[76],"has":[77],"shown":[78],"that":[79,132],"attacks,":[81,86],"such":[82],"as":[83],"attribute":[84,138],"inference":[85],"are":[87],"achievable":[88],"for":[89],"SER":[90,114,147],"systems":[91],"trained":[92],"using":[93],"FL.":[94,117],"work,":[97],"we":[98],"propose":[99],"evaluate":[101],"user-level":[103],"differential":[104],"(UDP)":[106],"mitigating":[108],"leaks":[111,167],"of":[112,145,159],"system":[115,148,166],"UDP":[118,134,161],"theoretical":[120],"guarantees":[122],"with":[123,149],"parameters":[125],"$\\epsilon$":[126],"and":[127],"$\\delta$.":[128],"Our":[129],"results":[130,183],"show":[131],"effectively":[136],"decrease":[137],"information":[139],"leakage":[140],"while":[141],"utility":[144],"accessing":[152],"one":[153],"model":[154,169],"update.":[155],"efficacy":[158],"suffers":[162],"when":[163],"more":[168],"updates":[170],"adversary.":[173],"We":[174],"make":[175],"code":[177],"publicly":[178],"available":[179],"reproduce":[181],"https://github.com/usc-sail/fed-ser-leakage.":[185]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":16}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}
